FROM THE RESEARCH
What the data says about AI adoption.
The hype is loud, but the studies are consistent — about what fails, what works, and where your data should live. Our approach is built on that evidence.
95
%
of enterprise AI pilots show no measurable P&L impact
Most pilots fail. The AI isn't why.
MIT researchers analyzed 300+ enterprise deployments and traced the failures not to the models, but to flawed integration — generic tools bolted onto processes that never changed. Read more ↗
OUR TAKE → We never install tools. We integrate workflows.
2×
the success rate when built with a specialized partner vs. in-house
Partners beat DIY. Back office beats flash.
The same study found externally-partnered builds reached production about twice as often as internal ones — and the steadiest returns came from back-office automation, not splashy front-office pilots. Read more ↗
OUR TAKE → We start where the hours leak: operations, accounting, reporting.
3×
more likely that top performers fundamentally redesigned workflows
Winners redesign work — they don't just add AI.
Across McKinsey's global survey, redesigning the workflow around AI was the single strongest predictor of bottom-line impact — ahead of model choice, budget, or team size. Read more ↗
OUR TAKE → This is exactly why we Assess before we Build.
0
of your proprietary data should end up training someone else's model
Your data is your alpha. Keep it home.
Your pricing, playbooks, and customer history are your edge — they don't belong in public cloud models. With RAG, your knowledge stays behind your firewall and the AI reads from it without keeping a copy. Read more ↗
OUR TAKE → Intelligence from the model. Memory on your premises.